Exercise 2_3_2: Spatial Regression

Let’s see how different neighborhood matrix styles can impact the estimates of spatial regression models. Again, run the code below in order to have the data in place for this exercises.

## Reading layer `OGRGeoJSON' from data source 
##   `https://geoportal.stadt-koeln.de/arcgis/rest/services/Basiskarten/kgg/MapServer/20/query?where=objectid+is+not+null&text=&objectIds=&time=&geometry=&geometryType=esriGeometryEnvelope&inSR=&spatialRel=esriSpatialRelIntersects&distance=&units=esriSRUnit_Foot&relationParam=&outFields=*&returnGeometry=true&returnTrueCurves=false&maxAllowableOffset=&geometryPrecision=&outSR=4326&havingClause=&returnIdsOnly=false&returnCountOnly=false&orderByFields=&groupByFieldsForStatistics=&outStatistics=&returnZ=false&returnM=false&gdbVersion=&historicMoment=&returnDistinctValues=false&resultOffset=&resultRecordCount=&returnExtentOnly=false&datumTransformation=&parameterValues=&rangeValues=&quantizationParameters=&featureEncoding=esriDefault&f=geojson' 
##   using driver `GeoJSON'
## Simple feature collection with 543 features and 20 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: 6.77253 ymin: 50.83045 xmax: 7.162028 ymax: 51.08496
## Geodetic CRS:  WGS 84

## ℹ Using "','" as decimal and "'.'" as grouping mark. Use `read_delim()` for more control.

## Rows: 949 Columns: 79
## ── Column specification ──────────────────────────────────────────────────────────────────────────────────────────
## Delimiter: ";"
## chr  (3): wahl, ags, gebiet-name
## dbl (71): gebiet-nr, max-schnellmeldungen, anz-schnellmeldungen, A1, A2, A3, A, B, B1, C, D, E, F, D1, F1, D2,...
## lgl  (4): D30, F30, D31, F31
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

voting_districts <-
  glue::glue(
    "https://geoportal.stadt-koeln.de/arcgis/rest/services/Basiskarten/kgg/\\
    MapServer/20/query?where=objectid+is+not+null&text=&objectIds=&time=&\\
    geometry=&geometryType=esriGeometryEnvelope&inSR=&spatialRel=\\
    esriSpatialRelIntersects&distance=&units=esriSRUnit_Foot&relationParam=&\\
    outFields=*&returnGeometry=true&returnTrueCurves=false&maxAllowableOffset=\\
    &geometryPrecision=&outSR=4326&havingClause=&returnIdsOnly=false&return\\
    CountOnly=false&orderByFields=&groupByFieldsForStatistics=&outStatistics=\\
    &returnZ=false&returnM=false&gdbVersion=&historicMoment=&returnDistinct\\
    Values=false&resultOffset=&resultRecordCount=&returnExtentOnly=false&datum\\
    Transformation=&parameterValues=&rangeValues=&quantizationParameters=&\\
    featureEncoding=esriDefault&f=geojson"
  ) %>% 
  sf::st_read(as_tibble = TRUE) %>%
  sf::st_transform(3035) %>% 
  dplyr::transmute(Stimmbezirk = as.numeric(nummer))

afd_votes <-
  glue::glue(
    "https://www.stadt-koeln.de/wahlen/bundestagswahl/09-2021/praesentation/\\
    Open-Data-Bundestagswahl476.csv"
  ) %>% 
  readr::read_csv2() %>%
  dplyr::transmute(Stimmbezirk = `gebiet-nr`, afd_share = (F1 / F) * 100)

election_results <-
  dplyr::left_join(
    voting_districts,
    afd_votes,
    by = "Stimmbezirk"
  )

immigrants_cologne <-
  z11::z11_get_100m_attribute(STAATSANGE_KURZ_2) %>%
  terra::crop(election_results) %>%
  terra::mask(terra::vect(election_results))


inhabitants_cologne <-
  z11::z11_get_100m_attribute(Einwohner) %>%
  terra::crop(election_results) %>%
  terra::mask(terra::vect(election_results))

immigrant_share_cologne <-
  (immigrants_cologne / inhabitants_cologne) * 100

election_results <-
  election_results %>%
  dplyr::mutate(
    immigrant_share = 
      exactextractr::exact_extract(immigrant_share_cologne, ., 'mean', progress = FALSE),
    inhabitants = 
      exactextractr::exact_extract(inhabitants_cologne, ., 'mean', progress = FALSE)
  )

1

Re-use the code from the previous exercise for the Queen neighborhoods. But this time do one weight matrix with row-normalization and another one with minmax-normalization. Insert them into two spatial regression lag models of your choice using the same variables as in the lecture, i.e., both of them should either be a Spatial Lag Y or Spatial Lag X model.

Clues

For minmax-normalization, you would have to use the option style = "minmax" in the spdep:nb2listw() function.

2

Calculate the impacts of both models. What is your observation?